LINEAR REGRESSION MODEL- TOTAL COST PER PATIENT INFLATION ADJUSTED DATA
INGUINAL HERNIA ALL 18 Facilities- 2015-2020
HMJ
Nov 08, 2021
##Reading Data
options(max.print=999999999)
options(warn=-1)
library(caret)
library(readstata13)
library(TSstudio)
library(caret)
library(ROCR)
library(pROC)
library(xgboost)
library(gbm)
library(dplyr)
library(gtsummary)
library(xlsx)
library(rJava)
library(ggplot2)
library(segmented)
library(rpart)
library(tidyverse)
library(modelr)
library(dotwhisker)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(broom)
library(lme4)
library(MuMIn)
library(AICcmodavg)
library(psycho)
library(performance)
library(pbkrtest)
library(estimability)
library(emmeans)
library(lsmeans)
library(ggiraph)
library(ggiraphExtra)
require(plyr)
data2<-read.dta13(file = "Y:/Haroon Janjua/Project_Lap_Open_Rob_FL AHCA_Charges/Data_iHernia/Hernia_18_Robotic_Inflation.dta")
# looking at the dimensions of the dataset
dim(data2)## [1] 8604 24# looking at any missing values in our dataset
sum(is.na(data2))## [1] 0# taking a look at the structure of the varaibles in the dataset
str(data2[1:24])## 'data.frame': 8604 obs. of 24 variables:
## $ SYS_RECID : num 62533124 62612463 62532927 65090208 63313825 ...
## $ MCARE_NBR : chr "100006" "100006" "100006" "100006" ...
## $ Tot_Open : num 202 202 202 202 202 202 202 202 202 202 ...
## $ Tot_Lap : num 66 66 66 66 66 66 66 66 66 66 ...
## $ Tot_Rob : num 11 11 11 11 11 11 11 11 11 11 ...
## $ Tot_Hernia : num 279 279 279 279 279 279 279 279 279 279 ...
## $ TCHGS : num 46136 26501 25429 42431 34879 ...
## $ OPRMCHGS : num 17676 13616 10716 18775 10281 ...
## $ Non_OPR_CHGS : num 28460 12885 14713 23656 24598 ...
## $ COST : num 7213 4143 3976 6634 5453 ...
## $ Cost_OPR : num 2764 2129 1675 2935 1607 ...
## $ COST_Real : num 7663 4402 4224 7048 5793 ...
## $ COST_OPR_Real : num 2936 2262 1780 3118 1708 ...
## $ Non_OPR_Cost : num 4450 2015 2300 3699 3846 ...
## $ CCRATIO : num 0.156 0.156 0.156 0.156 0.156 ...
## $ OPR_PC : num 0.383 0.514 0.421 0.442 0.295 ...
## $ OPR_PC_CHG : num 0.383 0.514 0.421 0.442 0.295 ...
## $ TOTAL_HOSPITAL_BEDS: int 1454 1454 1454 1454 1454 1454 1454 1454 1454 1454 ...
## $ VISITS : int 1222 4755 790 1373 778 4987 4755 1362 778 1373 ...
## $ TIME : num 3 3 3 3 3 3 3 3 3 3 ...
## $ typeofproc2 : chr "OPEN" "OPEN" "OPEN" "OPEN" ...
## $ typeofproc : num 1 1 1 1 1 2 1 1 1 1 ...
## $ YEAR : num 2017 2017 2017 2017 2017 ...
## $ QTR : int 1 1 1 4 2 2 1 2 2 4 ...data2[c(3:19)] <- lapply(data2[,c(3:19)] , as.numeric)
data2$typeofproc2 <- as.factor(data2$typeofproc2)
data2$typeofproc <- as.factor(data2$typeofproc)
# taking a look at the structure of the varaibles in the dataset
str(data2[1:24])## 'data.frame': 8604 obs. of 24 variables:
## $ SYS_RECID : num 62533124 62612463 62532927 65090208 63313825 ...
## $ MCARE_NBR : chr "100006" "100006" "100006" "100006" ...
## $ Tot_Open : num 202 202 202 202 202 202 202 202 202 202 ...
## $ Tot_Lap : num 66 66 66 66 66 66 66 66 66 66 ...
## $ Tot_Rob : num 11 11 11 11 11 11 11 11 11 11 ...
## $ Tot_Hernia : num 279 279 279 279 279 279 279 279 279 279 ...
## $ TCHGS : num 46136 26501 25429 42431 34879 ...
## $ OPRMCHGS : num 17676 13616 10716 18775 10281 ...
## $ Non_OPR_CHGS : num 28460 12885 14713 23656 24598 ...
## $ COST : num 7213 4143 3976 6634 5453 ...
## $ Cost_OPR : num 2764 2129 1675 2935 1607 ...
## $ COST_Real : num 7663 4402 4224 7048 5793 ...
## $ COST_OPR_Real : num 2936 2262 1780 3118 1708 ...
## $ Non_OPR_Cost : num 4450 2015 2300 3699 3846 ...
## $ CCRATIO : num 0.156 0.156 0.156 0.156 0.156 ...
## $ OPR_PC : num 0.383 0.514 0.421 0.442 0.295 ...
## $ OPR_PC_CHG : num 0.383 0.514 0.421 0.442 0.295 ...
## $ TOTAL_HOSPITAL_BEDS: num 1454 1454 1454 1454 1454 ...
## $ VISITS : num 1222 4755 790 1373 778 ...
## $ TIME : num 3 3 3 3 3 3 3 3 3 3 ...
## $ typeofproc2 : Factor w/ 3 levels "LAP","OPEN","ROBOTIC": 2 2 2 2 2 1 2 2 2 2 ...
## $ typeofproc : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 2 1 1 1 1 ...
## $ YEAR : num 2017 2017 2017 2017 2017 ...
## $ QTR : int 1 1 1 4 2 2 1 2 2 4 ...#Linear Regression Models
# fit LM for Open
set.seed(5842)
model.open <- lm(COST_Real ~ TIME , data = data2, subset = typeofproc2 == "OPEN")
summary(model.open)##
## Call:
## lm(formula = COST_Real ~ TIME, data = data2, subset = typeofproc2 ==
## "OPEN")
##
## Residuals:
## Min 1Q Median 3Q Max
## -3869.4 -816.9 -130.8 521.5 10661.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3783.09 47.92 78.95 <2e-16 ***
## TIME 179.57 13.75 13.06 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1278 on 3913 degrees of freedom
## Multiple R-squared: 0.04179, Adjusted R-squared: 0.04155
## F-statistic: 170.7 on 1 and 3913 DF, p-value: < 2.2e-16# fit LM for Lap
set.seed(5842)
model.lap <- lm(COST_Real ~ TIME , data = data2, subset = typeofproc2 == "LAP")
summary(model.lap)##
## Call:
## lm(formula = COST_Real ~ TIME, data = data2, subset = typeofproc2 ==
## "LAP")
##
## Residuals:
## Min 1Q Median 3Q Max
## -5093.3 -1041.4 -312.3 625.2 11916.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5155.21 97.47 52.892 <2e-16 ***
## TIME 240.92 25.26 9.539 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1710 on 1784 degrees of freedom
## Multiple R-squared: 0.04853, Adjusted R-squared: 0.048
## F-statistic: 90.99 on 1 and 1784 DF, p-value: < 2.2e-16# fit LM for Rob
set.seed(5842)
model.rob <- lm(COST_Real ~ TIME , data = data2, subset = typeofproc2 == "ROBOTIC")
summary(model.rob)##
## Call:
## lm(formula = COST_Real ~ TIME, data = data2, subset = typeofproc2 ==
## "ROBOTIC")
##
## Residuals:
## Min 1Q Median 3Q Max
## -4797.3 -1325.0 -314.6 1004.6 12400.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5876.84 126.14 46.59 <2e-16 ***
## TIME 357.76 30.08 11.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2091 on 2901 degrees of freedom
## Multiple R-squared: 0.04651, Adjusted R-squared: 0.04618
## F-statistic: 141.5 on 1 and 2901 DF, p-value: < 2.2e-16model.open$coefficients## (Intercept) TIME
## 3783.0915 179.5722model.lap$coefficients## (Intercept) TIME
## 5155.2086 240.9209model.rob$coefficients## (Intercept) TIME
## 5876.8365 357.7567#Analysis of Variance
set.seed(5842)
m.interaction <- lm(COST_Real ~ TIME*typeofproc2, data = data2)
anova(m.interaction)## Analysis of Variance Table
##
## Response: COST_Real
## Df Sum Sq Mean Sq F value Pr(>F)
## TIME 1 3.7662e+09 3766197301 1332.530 < 2.2e-16 ***
## typeofproc2 2 1.2076e+10 6037832136 2136.264 < 2.2e-16 ***
## TIME:typeofproc2 2 9.8491e+07 49245601 17.424 2.807e-08 ***
## Residuals 8598 2.4301e+10 2826352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.lst <- lstrends(m.interaction, "typeofproc2", var="TIME") ; m.lst## typeofproc2 TIME.trend SE df lower.CL upper.CL
## LAP 241 24.8 8598 192 290
## OPEN 180 18.1 8598 144 215
## ROBOTIC 358 24.2 8598 310 405
##
## Confidence level used: 0.95pairs(m.lst)## contrast estimate SE df t.ratio p.value
## LAP - OPEN 61.3 30.7 8598 1.998 0.1128
## LAP - ROBOTIC -116.8 34.7 8598 -3.372 0.0022
## OPEN - ROBOTIC -178.2 30.2 8598 -5.903 <.0001
##
## P value adjustment: tukey method for comparing a family of 3 estimates#Interactive Linear Model Plots
ggPredict(model.open,se=TRUE,interactive=TRUE)ggPredict(model.lap,se=TRUE,interactive=TRUE)ggPredict(model.rob,se=TRUE,interactive=TRUE)#Non Interactive Combined Linear Model Plots
data2 %>%
ggplot(aes(x=TIME,
y=COST_Real,
color=typeofproc2))+
geom_point()+
geom_smooth(method="lm")
data2 %>%
ggplot(aes(x=TIME,
y=COST_Real,
color=typeofproc2))+
geom_smooth(method="lm")
#Year by Year Linear Reg Models
data3<-read.dta13(file = "Y:/Haroon Janjua/Project_Lap_Open_Rob_FL AHCA_Charges/Data_iHernia/Hernia_18_Robotic_YEARLY_Inflation.dta")
# looking at the dimensions of the dataset
dim(data3)## [1] 11266 15# looking at any missing values in our dataset
sum(is.na(data3))## [1] 45064# taking a look at the structure of the varaibles in the dataset
str(data3[1:15])## 'data.frame': 11266 obs. of 15 variables:
## $ SYS_RECID : num 65308574 64540880 62533219 64539131 63274186 ...
## $ COST : num 13124 10914 13713 7286 9474 ...
## $ Cost_OPR : num 7312 6531 7810 2809 4804 ...
## $ COST_Real : num 13942 11595 14569 7740 10065 ...
## $ COST_OPR_Real: num 7768 6939 8297 2984 5103 ...
## $ Non_OPR_Cost : num 5812 4383 5904 4477 4670 ...
## $ TIME : num 3 3 3 3 3 3 3 3 3 3 ...
## $ typeofproc2 : chr "ROBOTIC" "ROBOTIC" "ROBOTIC" "ROBOTIC" ...
## $ typeofproc : num 3 3 3 3 3 3 3 3 3 3 ...
## $ YEAR : num 2017 2017 2017 2017 2017 ...
## $ typeofproc3 : num NA NA NA NA NA NA NA NA NA NA ...
## $ typeofproc4 : num NA NA NA NA NA NA NA NA NA NA ...
## $ typeofproc5 : num NA NA NA NA NA NA NA NA NA NA ...
## $ typeofproc6 : num NA NA NA NA NA NA NA NA NA NA ...
## $ typeproc2 : chr "ROB_15-20" "ROB_15-20" "ROB_15-20" "ROB_15-20" ...data3[c(2:6)] <- lapply(data3[,c(2:6)] , as.numeric)
data3$typeofproc2 <- as.factor(data3$typeofproc2)
data3$typeofproc <- as.factor(data3$typeofproc)
data3$typeproc2 <- as.factor(data3$typeproc2)
# taking a look at the structure of the varaibles in the dataset
str(data3[1:15])## 'data.frame': 11266 obs. of 15 variables:
## $ SYS_RECID : num 65308574 64540880 62533219 64539131 63274186 ...
## $ COST : num 13124 10914 13713 7286 9474 ...
## $ Cost_OPR : num 7312 6531 7810 2809 4804 ...
## $ COST_Real : num 13942 11595 14569 7740 10065 ...
## $ COST_OPR_Real: num 7768 6939 8297 2984 5103 ...
## $ Non_OPR_Cost : num 5812 4383 5904 4477 4670 ...
## $ TIME : num 3 3 3 3 3 3 3 3 3 3 ...
## $ typeofproc2 : Factor w/ 2 levels "","ROBOTIC": 2 2 2 2 2 2 2 2 2 2 ...
## $ typeofproc : Factor w/ 1 level "3": 1 1 1 1 1 1 1 1 1 1 ...
## $ YEAR : num 2017 2017 2017 2017 2017 ...
## $ typeofproc3 : num NA NA NA NA NA NA NA NA NA NA ...
## $ typeofproc4 : num NA NA NA NA NA NA NA NA NA NA ...
## $ typeofproc5 : num NA NA NA NA NA NA NA NA NA NA ...
## $ typeofproc6 : num NA NA NA NA NA NA NA NA NA NA ...
## $ typeproc2 : Factor w/ 5 levels "ROB_15-20","ROB_16-20",..: 1 1 1 1 1 1 1 1 1 1 ...# fit LM for Rob 2015-2020
set.seed(5877)
model.rob <- lm(COST_Real ~ TIME , data = data3, subset = typeproc2 == "ROB_15-20")
summary(model.rob)##
## Call:
## lm(formula = COST_Real ~ TIME, data = data3, subset = typeproc2 ==
## "ROB_15-20")
##
## Residuals:
## Min 1Q Median 3Q Max
## -4797.3 -1325.0 -314.6 1004.6 12400.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5876.84 126.14 46.59 <2e-16 ***
## TIME 357.76 30.08 11.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2091 on 2901 degrees of freedom
## Multiple R-squared: 0.04651, Adjusted R-squared: 0.04618
## F-statistic: 141.5 on 1 and 2901 DF, p-value: < 2.2e-16# fit LM for Rob 2016-2020
set.seed(5877)
model.rob16 <- lm(COST_Real ~ TIME , data = data3, subset = typeproc2 == "ROB_16-20")
summary(model.rob16)##
## Call:
## lm(formula = COST_Real ~ TIME, data = data3, subset = typeproc2 ==
## "ROB_16-20")
##
## Residuals:
## Min 1Q Median 3Q Max
## -4825.3 -1302.2 -303.6 1014.3 10728.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5595.57 134.69 41.55 <2e-16 ***
## TIME 419.61 31.77 13.21 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2066 on 2835 degrees of freedom
## Multiple R-squared: 0.05798, Adjusted R-squared: 0.05764
## F-statistic: 174.5 on 1 and 2835 DF, p-value: < 2.2e-16# fit LM for Rob 2017-2020
set.seed(5877)
model.rob17 <- lm(COST_Real ~ TIME ,data = data3, subset = typeproc2 == "ROB_17-20")
summary(model.rob17)##
## Call:
## lm(formula = COST_Real ~ TIME, data = data3, subset = typeproc2 ==
## "ROB_17-20")
##
## Residuals:
## Min 1Q Median 3Q Max
## -4852.9 -1322.1 -331.2 1009.6 10828.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4983.64 192.14 25.94 <2e-16 ***
## TIME 547.52 42.91 12.76 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2123 on 2469 degrees of freedom
## Multiple R-squared: 0.06186, Adjusted R-squared: 0.06148
## F-statistic: 162.8 on 1 and 2469 DF, p-value: < 2.2e-16# fit LM for Rob 2018-2020
set.seed(5877)
model.rob18 <- lm(COST_Real ~ TIME , data = data3, subset = typeproc2 == "ROB_18-20")
summary(model.rob18)##
## Call:
## lm(formula = COST_Real ~ TIME, data = data3, subset = typeproc2 ==
## "ROB_18-20")
##
## Residuals:
## Min 1Q Median 3Q Max
## -4839.9 -1592.2 -276.4 1112.0 10985.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4252.12 365.51 11.633 <2e-16 ***
## TIME 691.21 75.03 9.212 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2264 on 1851 degrees of freedom
## Multiple R-squared: 0.04384, Adjusted R-squared: 0.04332
## F-statistic: 84.87 on 1 and 1851 DF, p-value: < 2.2e-16# fit LM for Rob 2019-2020
set.seed(5877)
model.rob19 <- lm(COST_Real ~ TIME ,data = data3, subset = typeproc2 == "ROB_19-20")
summary(model.rob19)##
## Call:
## lm(formula = COST_Real ~ TIME, data = data3, subset = typeproc2 ==
## "ROB_19-20")
##
## Residuals:
## Min 1Q Median 3Q Max
## -4783.8 -1475.4 -293.8 1254.7 9424.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -362.8 777.8 -0.467 0.641
## TIME 1542.8 147.2 10.481 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2253 on 1200 degrees of freedom
## Multiple R-squared: 0.08386, Adjusted R-squared: 0.0831
## F-statistic: 109.8 on 1 and 1200 DF, p-value: < 2.2e-16model.rob$coefficients## (Intercept) TIME
## 5876.8365 357.7567model.rob16$coefficients## (Intercept) TIME
## 5595.5720 419.6061model.rob17$coefficients## (Intercept) TIME
## 4983.6387 547.5162model.rob18$coefficients## (Intercept) TIME
## 4252.1153 691.2144model.rob19$coefficients## (Intercept) TIME
## -362.8423 1542.7673#Analysis of Variance
m.interaction2 <- lm(COST_Real ~ TIME*typeproc2, data = data3)
anova(m.interaction2)## Analysis of Variance Table
##
## Response: COST_Real
## Df Sum Sq Mean Sq F value Pr(>F)
## TIME 1 2.9412e+09 2941172815 642.8818 <2e-16 ***
## typeproc2 4 2.3397e+07 5849365 1.2786 0.2758
## TIME:typeproc2 4 3.9959e+08 99896355 21.8354 <2e-16 ***
## Residuals 11256 5.1496e+10 4574982
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1m.interaction2$coefficients## (Intercept) TIME typeproc2ROB_16-20
## 5876.8365 357.7567 -281.2645
## typeproc2ROB_17-20 typeproc2ROB_18-20 typeproc2ROB_19-20
## -893.1978 -1624.7212 -6239.6788
## TIME:typeproc2ROB_16-20 TIME:typeproc2ROB_17-20 TIME:typeproc2ROB_18-20
## 61.8494 189.7595 333.4577
## TIME:typeproc2ROB_19-20
## 1185.0106m.lst2 <- lstrends(m.interaction2, "typeproc2", var="TIME") ; m.lst2## typeproc2 TIME.trend SE df lower.CL upper.CL
## ROB_15-20 358 30.8 11256 297 418
## ROB_16-20 420 32.9 11256 355 484
## ROB_17-20 548 43.2 11256 463 632
## ROB_18-20 691 70.9 11256 552 830
## ROB_19-20 1543 139.7 11256 1269 1817
##
## Confidence level used: 0.95pairs(m.lst2)## contrast estimate SE df t.ratio p.value
## (ROB_15-20) - (ROB_16-20) -61.8 45.0 11256 -1.374 0.6447
## (ROB_15-20) - (ROB_17-20) -189.8 53.1 11256 -3.576 0.0032
## (ROB_15-20) - (ROB_18-20) -333.5 77.3 11256 -4.315 0.0002
## (ROB_15-20) - (ROB_19-20) -1185.0 143.1 11256 -8.283 <.0001
## (ROB_16-20) - (ROB_17-20) -127.9 54.3 11256 -2.355 0.1281
## (ROB_16-20) - (ROB_18-20) -271.6 78.1 11256 -3.476 0.0046
## (ROB_16-20) - (ROB_19-20) -1123.2 143.5 11256 -7.825 <.0001
## (ROB_17-20) - (ROB_18-20) -143.7 83.0 11256 -1.731 0.4150
## (ROB_17-20) - (ROB_19-20) -995.3 146.3 11256 -6.805 <.0001
## (ROB_18-20) - (ROB_19-20) -851.6 156.7 11256 -5.435 <.0001
##
## P value adjustment: tukey method for comparing a family of 5 estimatesset.seed(5877)
data3 %>%
ggplot(aes(x=TIME,
y=COST_Real,
color=typeproc2))+
geom_point()+
geom_smooth(method="lm")
set.seed(5877)
data3 %>%
ggplot(aes(x=TIME,
y=COST_Real,
color=typeproc2))+
geom_smooth(method="lm")
#Linear Model with more variables
# Linear Regression Model of Cost
lm.Cost <- lm(COST_Real ~ Tot_Open +Tot_Lap+ Tot_Rob + TOTAL_HOSPITAL_BEDS + CCRATIO,
data = data2, subset = typeofproc2 == "ROBOTIC")
summary(lm.Cost) ##
## Call:
## lm(formula = COST_Real ~ Tot_Open + Tot_Lap + Tot_Rob + TOTAL_HOSPITAL_BEDS +
## CCRATIO, data = data2, subset = typeofproc2 == "ROBOTIC")
##
## Residuals:
## Min 1Q Median 3Q Max
## -5032.8 -1349.7 -279.5 1001.6 12222.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5750.6313 161.5542 35.596 < 2e-16 ***
## Tot_Open -9.9168 1.9254 -5.150 2.77e-07 ***
## Tot_Lap 12.5956 2.9187 4.315 1.65e-05 ***
## Tot_Rob 17.3132 1.6763 10.328 < 2e-16 ***
## TOTAL_HOSPITAL_BEDS 1.9109 0.2151 8.883 < 2e-16 ***
## CCRATIO 1090.6154 1454.4052 0.750 0.453
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2018 on 2897 degrees of freedom
## Multiple R-squared: 0.1136, Adjusted R-squared: 0.1121
## F-statistic: 74.29 on 5 and 2897 DF, p-value: < 2.2e-16# Linear Regression Model of Operating Cost
lm.CostOpr <- lm(COST_OPR_Real ~ Tot_Open +Tot_Lap+ Tot_Rob + TOTAL_HOSPITAL_BEDS + CCRATIO,
data = data2, subset = typeofproc2 == "ROBOTIC")
summary(lm.CostOpr) ##
## Call:
## lm(formula = COST_OPR_Real ~ Tot_Open + Tot_Lap + Tot_Rob + TOTAL_HOSPITAL_BEDS +
## CCRATIO, data = data2, subset = typeofproc2 == "ROBOTIC")
##
## Residuals:
## Min 1Q Median 3Q Max
## -3845.9 -1311.5 -304.5 1141.0 12125.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4587.5930 151.1116 30.359 < 2e-16 ***
## Tot_Open -21.3015 1.8010 -11.828 < 2e-16 ***
## Tot_Lap 0.3128 2.7301 0.115 0.909
## Tot_Rob 8.7389 1.5679 5.573 2.73e-08 ***
## TOTAL_HOSPITAL_BEDS 1.5105 0.2012 7.507 8.03e-14 ***
## CCRATIO 46.8899 1360.3951 0.034 0.973
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1887 on 2897 degrees of freedom
## Multiple R-squared: 0.08344, Adjusted R-squared: 0.08186
## F-statistic: 52.75 on 5 and 2897 DF, p-value: < 2.2e-16